Nobel Prize Winner, Machine Learning Guru, and Facebook Data Scientist to Keynote 2015 IEEE ICDM

LOS ALAMITOS, Calif., 20 March 2015 – Robert F. Engle, co-recipient of the Nobel Prize for Economics; Michael I. Jordan, a guru in machine learning; and Lada Adamic, a computational social scientist at Facebook, will be keynote speakers for the 2015 IEEE International Conference on Data Mining series (ICDM), the world’s premier research conference in data mining.  
 
Engle conducted much of his prizewinning work in the 1970s and 80s, when he developed improved mathematical techniques for risk evaluation and forecasting, which helped better understand stock market volatility. Engle and Clive W.J. Granger received the Nobel Prize for Economics in 2003 for their development of methods for analyzing time series data with time-varying volatility.
 
Michael I. Jordan is the Pehong Chen Distinguished Professor in the Department of Electrical Engineering and Computer Science and the Department of Statistics at the University of California, Berkeley. Jordan is a member of the National Academy of Sciences, National Academy of Engineering and American Academy of Arts and Sciences. His research interests bridge the computational, statistical, cognitive, and biological sciences.
 
Adamic leads the Product Science group within Facebook’s Data Science Team. She is also an adjunct associate professor at University of Michigan’s School of Information and Center for the Study of Complex Systems. Her projects have included identifying expertise in online question and answer forums, studying the dynamics of viral marketing, and characterizing the structural and communication patterns in online social media. 
 
The IEEE International Conference on Data Mining series (ICDM) provides an international forum for presentation of original research results, as well as exchange and dissemination of innovative, practical development experiences. The conference covers all aspects of data mining, including algorithms, software and systems, and applications.
 
ICDM draws researchers and application developers from a wide range of data mining related areas such as statistics, machine learning, pattern recognition, databases and data warehousing, data visualization, knowledge-based systems, and high-performance computing. By promoting novel, high quality research findings, and innovative solutions to challenging data mining problems, the conference seeks to continuously advance the state-of-the-art in data mining. 
 
Besides the technical program, the conference features workshops, tutorials, panels and, since 2007, the ICDM data mining contest. ICDM contest proposals are due March 29. The deadline for full paper submissions is June 3. For more information, visit http://icdm2015.stonybrook.edu/.
 

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